0.1 By Month

0.2 CCAMLR Units

0.3 Distance

0.4 Time

0.5 Velocity

0.6 Angles

1 Correlated random walk

Process Model

\[ d_{t} \sim T*d_{t-1} + Normal(0,\Sigma)\] \[ x_t = x_{t-1} + d_{t} \]

1.1 Parameters

For each individual:

\[\theta = \text{Mean turning angle}\] \[\gamma = \text{Move persistence} \]

For both behaviors process variance is: \[ \sigma_{latitude} = 0.1\] \[ \sigma_{longitude} = 0.1\]

1.2 Behavioral States

\[ \text{For each individual i}\] \[ Behavior_1 = \text{traveling}\] \[ Behavior_2 = \text{foraging}\]

\[ \alpha_{i,1,1} = \text{Probability of remaining traveling when traveling}\] \[\alpha_{i,2,1} = \text{Probability of switching from Foraging to traveling}\]

\[\begin{matrix} \alpha_{i,1,1} & 1-\alpha_{i,1,1} \\ \alpha_{i,2,1} & 1-\alpha_{i,2,1} \\ \end{matrix}\]

With the probability of switching states:

\[logit(\phi_{traveling}) = \alpha_{Behavior_{t-1}}\]

\[\phi_{foraging} = 1 - \phi_{traveling} \]

1.3 Continious tracks

The transmitter will often go dark for 10 to 12 hours, due to weather, right in the middle of an otherwise good track. The model requires regular intervals to estimate the turning angles and temporal autocorrelation. As a track hits one of these walls, call it the end of a track, and begin a new track once the weather improves. We can remove any micro-tracks that are less than three days. Specify a duration, calculate the number of tracks and the number of removed points. Iteratively.

How did the filter change the extent of tracks?

Look at the observations were defined into tracks.

sink(“Bayesian/Multi_RW.jags”) cat(" model{

#Constants
pi <- 3.141592653589

#for each if 6 argos class observation error

for(x in 1:6){

  ##argos observation error##
  argos_prec[x,1:2,1:2] <- argos_cov[x,,]
  
  #Constructing the covariance matrix
  argos_cov[x,1,1] <- argos_sigma[x]
  argos_cov[x,1,2] <- 0
  argos_cov[x,2,1] <- 0
  argos_cov[x,2,2] <- argos_alpha[x]
}

for(i in 1:ind){
  for(g in 1:tracks[i]){
  
  ## Priors for first true location
  #for lat long
  y[i,g,1,1:2] ~ dmnorm(argos[i,g,1,1,1:2],argos_prec[1,1:2,1:2])
  
  #First movement - random walk.
  y[i,g,2,1:2] ~ dmnorm(y[i,g,1,1:2],iSigma)
  
  ###First Behavioral State###
  state[i,g,1] ~ dcat(lambda[]) ## assign state for first obs
  
  #Process Model for movement
  for(t in 2:(steps[i,g]-1)){
  
  #Behavioral State at time T
  phi[i,g,t,1] <- alpha_mu[state[i,g,t-1]] 
  phi[i,g,t,2] <- 1-phi[i,g,t,1]
  state[i,g,t] ~ dcat(phi[i,g,t,])
  
  #Turning covariate
  #Transition Matrix for turning angles
  T[i,g,t,1,1] <- cos(theta[state[i,g,t]])
  T[i,g,t,1,2] <- (-sin(theta[state[i,g,t]]))
  T[i,g,t,2,1] <- sin(theta[state[i,g,t]])
  T[i,g,t,2,2] <- cos(theta[state[i,g,t]])
  
  #Correlation in movement change
  d[i,g,t,1:2] <- y[i,g,t,] + gamma[state[i,g,t]] * T[i,g,t,,] %*% (y[i,g,t,1:2] - y[i,g,t-1,1:2])
  
  #Gaussian Displacement
  y[i,g,t+1,1:2] ~ dmnorm(d[i,g,t,1:2],iSigma)
  }
  
  #Final behavior state
  phi[i,g,steps[i,g],1] <- alpha_mu[state[i,g,steps[i,g]-1]] 
  phi[i,g,steps[i,g],2] <- 1-phi[i,g,steps[i,g],1]
  state[i,g,steps[i,g]] ~ dcat(phi[i,g,steps[i,g],])
  
  ##    Measurement equation - irregular observations
  # loops over regular time intervals (t)    
  
  for(t in 2:steps[i,g]){
  
  # loops over observed locations within interval t
  for(u in 1:idx[i,g,t]){ 
  zhat[i,g,t,u,1:2] <- (1-j[i,g,t,u]) * y[i,g,t-1,1:2] + j[i,g,t,u] * y[i,g,t,1:2]
  
  #for each lat and long
  #argos error
  #ag<-
  argos[i,g,t,u,1:2] ~ dmnorm(zhat[i,g,t,u,1:2],argos_prec[argos_class[i,g,t,u],1:2,1:2])
  }
  }
  }
}
###Priors###

#Process Variance
iSigma ~ dwish(R,2)
Sigma <- inverse(iSigma)

##Mean Angle
tmp[1] ~ dbeta(10, 10)
tmp[2] ~ dbeta(10, 10)

# prior for theta in 'traveling state'
theta[1] <- (2 * tmp[1] - 1) * pi

# prior for theta in 'foraging state'    
theta[2] <- (tmp[2] * pi * 2)

##Move persistance
# prior for gamma (autocorrelation parameter)
#from jonsen 2016

##Behavioral States

gamma[1] ~ dbeta(3,2)       ## gamma for state 1
dev ~ dbeta(1,1)            ## a random deviate to ensure that gamma[1] > gamma[2]
gamma[2] <- gamma[1] * dev

#Intercepts
alpha_mu[1] ~ dbeta(1,1)
alpha_mu[2] ~ dbeta(1,1)

#Probability of behavior switching 
lambda[1] ~ dbeta(1,1)
lambda[2] <- 1 - lambda[1]

##Argos priors##
#longitudinal argos precision, from Jonsen 2005, 2016, represented as precision not sd

#by argos class
argos_sigma[1] <- 11.9016
argos_sigma[2] <- 10.2775
argos_sigma[3] <- 1.228984
argos_sigma[4] <- 2.162593
argos_sigma[5] <- 3.885832
argos_sigma[6] <- 0.0565539

#latitidunal argos precision, from Jonsen 2005, 2016
argos_alpha[1] <- 67.12537
argos_alpha[2] <- 14.73474
argos_alpha[3] <- 4.718973
argos_alpha[4] <- 0.3872023
argos_alpha[5] <- 3.836444
argos_alpha[6] <- 0.1081156

}"
,fill=TRUE)

sink()

##      user    system   elapsed 
##   528.134     6.301 20678.711

1.4 Chains

##                               Type       Size    PrettySize  Rows Columns
## jagM                rjags.parallel 3180217112    [1] "3 Gb"     6      NA
## data                          list   80981360 [1] "77.2 Mb"    10      NA
## argos                        array   39720952 [1] "37.9 Mb"    33      20
## obs                          array   39720952 [1] "37.9 Mb"    33      20
## argos_class                  array   19867968 [1] "18.9 Mb"    33      20
## j                            array   19867968 [1] "18.9 Mb"    33      20
## obs_class                    array   19867968 [1] "18.9 Mb"    33      20
## b           SpatialPointsDataFrame   16418080 [1] "15.7 Mb" 46421      47
## mdat                    data.frame   16339200 [1] "15.6 Mb" 49859      47
## m                            ggmap   13116240 [1] "12.5 Mb"  1280    1280
##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   1790210   95.7    3886542  207.6   3886542  207.6
## Vcells 458848171 3500.8 1032714783 7879.0 890967591 6797.6

Look at the convergence of phi, just for an example

Overall relationship between phi and state, nice test of convergence.

1.4.1 Compare to priors

1.5 Parameter Summary

##   parameter         par       mean        lower      upper
## 1  alpha_mu alpha_mu[1] 0.90536385  0.877351886 0.92848037
## 2  alpha_mu alpha_mu[2] 0.04673335  0.034270429 0.06224137
## 3     gamma    gamma[1] 0.85421528  0.825441581 0.88482701
## 4     gamma    gamma[2] 0.29897117  0.225189373 0.36825143
## 5     theta    theta[1] 0.01277826 -0.009047288 0.03507419
## 6     theta    theta[2] 3.15056776  2.957943727 3.37065860

2 Behavioral Prediction

Relationship between phi and state

2.1 Spatial Prediction

2.2 By individual

Overlay phi and state

2.3 Compared to CMLRR regions

2.4 Autocorrelation in behavior

2.5 Location of Behavior

3 Overlap with Krill Fishery

4 Time spent in grid cell

4.1 ARS

##          All Behaviors              Traveling Area-restricted Search 
##             0.09285648            -0.05510307             0.17112208

4.1.1 Time by management unit

##                   Type      Size     PrettySize     Rows Columns
## pc              tbl_df 598231464 [1] "570.5 Mb" 11490000      10
## a               tbl_df 157774144 [1] "150.5 Mb"  3944000       7
## data              list  80981360  [1] "77.2 Mb"       10      NA
## argos            array  39720952  [1] "37.9 Mb"       33      20
## obs              array  39720952  [1] "37.9 Mb"       33      20
## argos_class      array  19867968  [1] "18.9 Mb"       33      20
## j                array  19867968  [1] "18.9 Mb"       33      20
## obs_class        array  19867968  [1] "18.9 Mb"       33      20
## mdat        data.frame  16339200  [1] "15.6 Mb"    49859      47
## temp             ggmap  13116528  [1] "12.5 Mb"     1280    1280
##             used  (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells   1679680  89.8    5103933  272.6    9968622  532.4
## Vcells 121359676 926.0  338399979 2581.8 1030480797 7862.0